Factors Affecting the Adoption of Environmental Management Systems by Crop and Livestock Farms in Canada
Bibliographic record
Abstract
This study examines, both qualitative and quantitatively, the motivation for crop, livestock, and mixed (both crop and livestock) farms in Canada to behave environmentally responsibly by adopting Environmental Management Systems (EMS) in the farm and the impact of a number of human capital, financial, farm structure, and social characteristics of the farmer and/or the farm on this behavior. It uses the data from 16,053 farms that responded to the Farm Environmental Management Survey conducted by Statistics Canada and Agriculture and Agri-Food Canada in 2001, which collects information on implementation of EMS in the areas manure, fertilizer, pesticide, water, wildlife, grazing, and nutrient management in the farm. The outcome of analysis show that mixed farms have the highest adoption rates, in general, across the eight EMSs considered in this study, while livestock-only farms have the lowest. The most common EMSs used by all farms are fertilizer and pesticide management plans with the whole farm environmental plan as the least likely to be adopted. The results based on a regression analysis suggest that “young” and “rich” farmers with a “large” land extent tend to adopt as many as possible EMS, but the gender of the farmer does not show a significant impact on this behaviour. The level of urbanization and government regulation also affects significantly the level of adoption of EMSs. The analysis, as a whole, points out that even in the absence of “mandatory” national level policies to regulate agricultural farms in Canada, farmers show a tendency to adopt as much as possible EMS “voluntarily”, because of their own interests in the farming environment and/or motives originating from the market where they operated with. DOI: http://dx.doi.org/10.4038/sjae.v6i1.3468 SJAE 2004; 6(1): 25-36
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".